Airtable Data Scientist Interview Questions + Guide in 2025

Airtable is a no-code app platform that empowers organizations to streamline and accelerate their most critical business processes, with over 500,000 companies, including a significant portion of the Fortune 100, utilizing its innovative solutions.

As a Data Scientist at Airtable, you will play a pivotal role in transforming raw data into actionable insights that inform product development and strategic business decisions. Your key responsibilities will include collaborating with cross-functional teams to uncover insights, developing and managing experiments, and contributing to the overall data culture of the organization. The ideal candidate will have over 6 years of experience in data science, data analysis, or engineering, with a strong proficiency in SQL and programming languages such as R or Python. You will need to exhibit a natural curiosity and the ability to communicate complex data findings effectively, ensuring that insights lead to informed business strategies. Your work will directly impact the way Airtable understands user behavior and optimizes its offerings, aligning with the company's commitment to empowering users through data-driven solutions.

This guide will equip you with targeted insights and preparation strategies, enhancing your confidence and performance during the interview process for the Data Scientist role at Airtable.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Airtable. The interview process will likely focus on your technical skills, analytical thinking, and ability to communicate insights effectively. Be prepared to discuss your past experiences, particularly how you have used data to drive business decisions and improve processes.

Technical Skills

1. Can you explain the difference between a UNION and a JOIN in SQL?

Understanding SQL is crucial for this role, and being able to articulate the differences between these two operations is essential.

How to Answer

Discuss the fundamental differences in how UNION combines datasets versus how JOIN merges them based on keys. Provide examples to illustrate your point.

Example

“A UNION combines the results of two or more SELECT statements into a single result set, while a JOIN merges rows from two or more tables based on a related column. For instance, if I have two tables of customer data, a UNION would stack the results vertically, while a JOIN would combine them horizontally based on a common customer ID.”

2. How do you optimize a SQL query for performance?

This question assesses your ability to write efficient SQL queries, which is vital for handling large datasets.

How to Answer

Mention techniques such as indexing, avoiding SELECT *, and using WHERE clauses effectively. Discuss your experience with query execution plans.

Example

“To optimize a SQL query, I focus on indexing the columns used in WHERE clauses and JOIN conditions. I also avoid using SELECT * and instead specify only the columns I need. Additionally, I analyze the query execution plan to identify bottlenecks and make adjustments accordingly.”

3. Describe a time you used statistical methods to solve a business problem.

This question evaluates your practical application of statistics in a business context.

How to Answer

Share a specific example where you applied statistical techniques to derive insights or make decisions. Highlight the impact of your work.

Example

“In my previous role, I used regression analysis to identify factors affecting customer churn. By analyzing historical data, I discovered that customers who engaged with our support team were less likely to leave. This insight led to a targeted retention strategy that reduced churn by 15%.”

4. What is A/B testing, and how do you design an effective experiment?

A/B testing is a common method for evaluating changes in products or features, and understanding its design is crucial.

How to Answer

Explain the concept of A/B testing and the key elements of a well-designed experiment, including control groups, sample size, and metrics.

Example

“A/B testing involves comparing two versions of a webpage or product to determine which performs better. To design an effective experiment, I ensure that I have a clear hypothesis, define success metrics, and use random sampling to minimize bias. I also calculate the required sample size to ensure statistical significance.”

5. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data analysis, and your approach can significantly affect results.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“When faced with missing data, I first assess the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques, such as mean or median substitution, or I may choose to exclude records with missing values if they are minimal. I also consider using models that can handle missing data directly.”

Product and Business Insights

1. How do you prioritize competing data requests from different teams?

This question assesses your ability to manage multiple stakeholders and prioritize effectively.

How to Answer

Explain your approach to understanding the business impact of each request and how you communicate with stakeholders to align priorities.

Example

“I prioritize data requests by assessing their potential impact on business goals. I engage with stakeholders to understand their needs and timelines, and I use a scoring system to evaluate the urgency and importance of each request. This helps me allocate my time effectively and ensure that high-impact projects are completed first.”

2. Describe a time when your analysis led to a significant business decision.

This question evaluates your ability to translate data insights into actionable business strategies.

How to Answer

Share a specific example where your analysis had a direct impact on a business decision, including the context, your analysis, and the outcome.

Example

“In a previous role, I analyzed user engagement data and discovered that a significant portion of our users were dropping off during the onboarding process. I presented my findings to the product team, which led to a redesign of the onboarding experience. As a result, we saw a 25% increase in user retention within the first month.”

3. How do you ensure that your data storytelling is effective?

This question assesses your communication skills and ability to present data insights clearly.

How to Answer

Discuss the importance of tailoring your message to your audience and using visualizations to enhance understanding.

Example

“To ensure effective data storytelling, I tailor my presentations to the audience’s level of expertise. I use clear visuals, such as charts and graphs, to highlight key insights and avoid jargon. I also focus on the ‘so what’ of my analysis, explaining how the findings can drive business decisions.”

4. What metrics do you consider most important for measuring product success?

This question evaluates your understanding of key performance indicators (KPIs) relevant to product development.

How to Answer

Discuss the importance of aligning metrics with business objectives and provide examples of relevant KPIs.

Example

“I believe that metrics such as user engagement, retention rates, and customer satisfaction are crucial for measuring product success. For instance, tracking the Net Promoter Score (NPS) can provide insights into customer loyalty, while monitoring daily active users (DAU) helps assess engagement levels.”

5. How do you approach building a data culture within an organization?

This question assesses your understanding of the importance of data-driven decision-making and your role in fostering a data culture.

How to Answer

Discuss strategies for promoting data literacy, collaboration, and the use of data in decision-making processes.

Example

“To build a data culture, I focus on promoting data literacy across the organization by conducting training sessions and workshops. I also encourage collaboration between teams by creating cross-functional data committees that share insights and best practices. By demonstrating the value of data-driven decisions, I aim to foster a culture where data is integral to our processes.”

Here are some tips to help you excel in your interview.

Understand the Interview Structure

Airtable's interview process can be lengthy and may include multiple rounds, including an initial interview focused on metrics, product sense, and SQL, followed by an onsite interview with various technical and case study questions. Familiarize yourself with the structure and prepare accordingly. Be ready for a presentation of a take-home assignment, as this is a significant part of the evaluation process.

Prepare for Product Case Questions

Expect to encounter product case questions that require you to think critically about how data can drive product decisions. These questions may not be explicitly mentioned by the recruiter, so proactively prepare for them. Develop a framework for approaching product-related problems and practice articulating your thought process clearly.

Brush Up on SQL and Data Analysis Skills

Given the emphasis on SQL in the interview process, ensure you are comfortable with complex queries, including window functions, joins, and aggregations. Practice writing SQL queries that analyze large datasets and derive meaningful insights. Additionally, be prepared to discuss your experience with data analysis tools and methodologies, including statistical concepts and experimental design.

Communicate Your Insights Effectively

Airtable values storytelling with data. During the interview, focus on how you can translate complex data findings into actionable insights. Practice explaining your past projects and analyses in a way that highlights your ability to communicate effectively with both technical and non-technical audiences.

Emphasize Collaboration and Cross-Functional Work

As a Data Scientist at Airtable, you will work closely with Product Development teams and executive leadership. Highlight your experience in cross-functional collaboration and how you have successfully partnered with different teams to drive insights and business outcomes. Be prepared to discuss specific examples of how you have influenced decision-making through data.

Cultivate a Curiosity-Driven Mindset

Airtable seeks candidates who possess a natural curiosity and a desire to uncover deeper relationships within data. During the interview, demonstrate your inquisitive nature by asking insightful questions about the company's data practices and challenges. Show that you are not just focused on the surface-level data but are eager to explore and understand the underlying patterns.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities, adaptability, and how you handle ambiguity. Prepare examples from your past experiences that showcase your analytical thinking, resilience, and ability to thrive in a fast-paced environment. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Align with Airtable's Culture

Airtable values diversity and inclusion, so be sure to convey your alignment with these principles during the interview. Share experiences that demonstrate your commitment to fostering an inclusive environment and how you have contributed to building a positive team culture in previous roles.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at Airtable. Good luck!

The interview process for a Data Scientist role at Airtable is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the dynamic environment of the company. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial screening, usually conducted via a phone call with a recruiter. This conversation lasts about 30 minutes and focuses on your background, experience, and motivation for applying to Airtable. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role. Be prepared to discuss your previous projects and how they relate to the responsibilities outlined in the job description.

2. Technical Interview

Following the initial screening, candidates typically undergo a technical interview. This may be conducted via video call and lasts approximately 45 minutes to an hour. During this session, you can expect to answer questions related to SQL, statistics, and experimental design. You may also be asked to solve case studies that require you to demonstrate your analytical thinking and problem-solving skills. Familiarity with SQL queries and data manipulation techniques is crucial, as you may be asked to explain your thought process in detail.

3. Onsite Interviews

The onsite interview process is more comprehensive and usually consists of multiple rounds, each lasting about an hour. These rounds will cover a range of topics, including product sense, experimentation, and advanced SQL queries. You may also be required to present a take-home project or analysis that you completed prior to the interview. This presentation is an opportunity to showcase your ability to communicate complex data insights clearly and effectively. Expect to engage with various team members, including data scientists, product managers, and possibly executives, to assess your fit within the team and the company.

4. Behavioral Interview

In addition to technical assessments, there will be a behavioral interview component. This part of the process focuses on your interpersonal skills, teamwork, and how you handle challenges. Be prepared to discuss past experiences where you demonstrated leadership, collaboration, and adaptability. The interviewers will be looking for evidence of your ability to thrive in a fast-paced, collaborative environment.

5. Final Interview

The final stage may involve a conversation with senior leadership or the hiring manager. This interview is often more strategic, focusing on how your skills and experiences align with Airtable's goals and vision. You may be asked about your long-term career aspirations and how you see yourself contributing to the company's growth.

As you prepare for your interviews, keep in mind the types of questions that may arise in each stage, particularly those related to your technical expertise and your ability to communicate insights effectively.

1. What metrics/graphs/models would you use to analyze churn behavior for Netflix’s pricing plans?

Netflix has two pricing plans: $15/month or $100/year. An executive wants to analyze the churn behavior of users subscribing to either plan. What kinds of metrics, graphs, and models would you build to provide an overarching view of subscription performance?

2. How would you build a model to predict which merchants DoorDash should acquire in a new market?

As a data scientist at DoorDash, how would you build a model to predict which merchants the company should target for acquisition when entering a new market?

3. How would you value the benefit of keeping a hit TV show on Netflix?

Netflix executives are considering renewing a deal with another TV network for exclusive streaming rights to a hit TV series. The show has been on Netflix for a year. How would you approach valuing the benefit of keeping this show on Netflix?

4. How would you measure and address the success of LinkedIn’s newsfeed ranking algorithm?

1. How would you measure the success of LinkedIn’s newsfeed ranking algorithm?
2. If some success metrics for the newsfeed algorithm are increasing while others are decreasing, how would you approach this?

5. How would you determine if the results of an AB test for a landing page redesign are statistically significant?

We want to launch a redesign of a landing page to improve the click-through rate using an AB test. How would you infer if the results of the click-through rate were statistically significant or not?

6. Write a function calculate_rmse to calculate the root mean squared error of a regression model.

The function should take in two lists, one that represents the predictions y_pred and another with the target values y_true.

7. Write a query to get the last transaction for each day from a table of bank transactions.

Given a table of bank transactions with columns id, transaction_value, and created_at, write a query to get the last transaction for each day. The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.

8. Write a function random_key that returns a key at random with a probability proportional to the weights.

Given a dictionary with weights, write a function random_key that returns a key at random with a probability proportional to the weights.

9. Write a function to get a sample from a standard normal distribution.

Create a function to generate a sample from a standard normal distribution.

10. Write an efficient function nearest_entries to find the closest element to N and its k-next and k-previous elements in a sorted list.

Given a sorted list of integers ints with no duplicates, write an efficient function nearest_entries that takes in integers N and k and finds the element closest to N, returning that element along with the k-next and k-previous elements of the list.

11. How would you explain what a p-value is to someone who is not technical?

Explain the concept of a p-value in simple terms to a non-technical person, focusing on its role in determining the significance of results in hypothesis testing.

12. How many more samples are needed to decrease the margin of error from 3 to 0.3?

Given a sample size (n) with a margin of error of 3, calculate the additional number of samples required to reduce the margin of error to 0.3.

13. How would you determine if the results of an AB test on click-through rate are statistically significant?

Describe the process of analyzing AB test results to determine if the observed differences in click-through rates are statistically significant.

14. How would you assign point values to letters in Spanish Scrabble if you don’t know Spanish?

If you need to build Scrabble for Spanish users and don’t know Spanish, how would you determine the point values for each letter?

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Airtable data scientist interview include:

• Understand Airtable Products: Study Airtable’s range of products and use cases across different industries. Be ready to discuss how your skills can aid in driving Airtable’s business goals through data.
• Be Prepared for SQL Questions: Brush up on your SQL knowledge, specifically focusing on window functions, joins, and unions. Make sure you can explain when and why you would use these functions.
• Practice Analytical Storytelling: Airtable values the ability to translate raw data into actionable insights. Practice storytelling with data, ensuring you can communicate complex data insights clearly and effectively to a non-technical audience.

What is the average salary for a Data Scientist at Airtable?

According to Glassdoor, Data Scientists at Airtable earn between $149K to $226K per year, with an average of $183K per year.

What kind of skills and experiences are required for the Data Scientist role at Airtable?

Candidates should have 6-10 years of experience, preferably working with leadership teams at high-growth startups. Proficiency in SQL and tools like R or Python is required. Ability to build self-service data sets in Looker and excellent communication skills to translate complex data into actionable insights are also important.

What are the primary responsibilities of a Data Scientist at Airtable?

Data Scientists at Airtable drive in-depth financial and product analysis, develop executive dashboards, and provide analytical insights to the CEO and Leadership Team. They also support product development teams, manage the design and analysis of experiments, and build a strong data culture within the company.

What should I expect in terms of compensation for the Data Scientist role at Airtable?

Compensation varies based on location, skills, and experience. For work locations in the San Francisco Bay Area, New York City, and Los Angeles, the base salary ranges from $170,000 to $221,500 USD. For all other locations, including remote, the range is $153,000 to $199,300 USD. The package includes benefits, restricted stock units, and may include incentive compensation.

Considering a Data Scientist role at Airtable? As a candidate, you’ll be pivotal in shaping the future of how users interact with their innovative, no-code software creation platform. Despite some mixed experiences shared by applicants—ranging from comprehensive interviews to unexpected questions—this role promises high visibility and significant impact. If you seek to influence strategic decisions and drive insights across product and finance teams, Airtable offers a compelling opportunity.

For more insights about the company, check out our main Airtable Interview Guide. We’ve covered many potential interview questions, and crafted guides for other roles such as software engineer and data analyst, to help you navigate the process.

Good luck with your interview!